diff --git a/yfinance/base.py b/yfinance/base.py
index d01c4e69c..406856a67 100644
--- a/yfinance/base.py
+++ b/yfinance/base.py
@@ -695,7 +695,7 @@ def _reconstruct_intervals_batch(self, df, interval, prepost, tag=-1):
                 f_tag = df_block_calib['Adj Close'] == tag
                 if f_tag.any():
                     div_adjusts = df_block_calib['Adj Close'] / df_block_calib['Close']
-                    # The loop below assumes each 1d repair is isoloated, i.e. surrounded by
+                    # The loop below assumes each 1d repair is isolated, i.e. surrounded by
                     # good data. Which is case most of time.
                     # But in case are repairing a chunk of bad 1d data, back/forward-fill the
                     # good div-adjustments - not perfect, but a good backup.
@@ -707,26 +707,30 @@ def _reconstruct_intervals_batch(self, df, interval, prepost, tag=-1):
                         if df_new.loc[dt, "Dividends"] != 0:
                             if idx < n - 1:
                                 # Easy, take div-adjustment from next-day
-                                div_adjusts[idx] = div_adjusts.iloc[idx + 1]
+                                div_adjusts.iloc[idx] = div_adjusts.iloc[idx + 1]
                             else:
                                 # Take previous-day div-adjustment and reverse todays adjustment
                                 div_adj = 1.0 - df_new_calib["Dividends"].iloc[idx] / df_new_calib['Close'].iloc[
                                     idx - 1]
-                                div_adjusts[idx] = div_adjusts.iloc[idx - 1] / div_adj
+                                div_adjusts.iloc[idx] = div_adjusts.iloc[idx - 1] / div_adj
                         else:
                             if idx > 0:
                                 # Easy, take div-adjustment from previous-day
-                                div_adjusts[idx] = div_adjusts.iloc[idx - 1]
+                                div_adjusts.iloc[idx] = div_adjusts.iloc[idx - 1]
                             else:
                                 # Must take next-day div-adjustment
-                                div_adjusts[idx] = div_adjusts.iloc[idx + 1]
+                                div_adjusts.iloc[idx] = div_adjusts.iloc[idx + 1]
                                 if df_new_calib["Dividends"].iloc[idx + 1] != 0:
-                                    div_adjusts[idx] *= 1.0 - df_new_calib["Dividends"].iloc[idx + 1] / \
+                                    div_adjusts.iloc[idx] *= 1.0 - df_new_calib["Dividends"].iloc[idx + 1] / \
                                                         df_new_calib['Close'].iloc[idx]
                     f_close_bad = df_block_calib['Close'] == tag
+                    div_adjusts = div_adjusts.reindex(df_block.index, fill_value=np.nan).ffill().bfill()
                     df_new['Adj Close'] = df_block['Close'] * div_adjusts
                     if f_close_bad.any():
-                        df_new.loc[f_close_bad, 'Adj Close'] = df_new['Close'][f_close_bad] * div_adjusts[f_close_bad]
+                        f_close_bad_new = f_close_bad.reindex(df_new.index, fill_value=False)
+                        div_adjusts_new = div_adjusts.reindex(df_new.index, fill_value=np.nan).ffill().bfill()
+                        div_adjusts_new_np = f_close_bad_new.to_numpy()
+                        df_new.loc[div_adjusts_new_np, 'Adj Close'] = df_new['Close'][div_adjusts_new_np] * div_adjusts_new[div_adjusts_new_np]
 
             # Check whether 'df_fine' has different split-adjustment.
             # If different, then adjust to match 'df'